African fuzzy Logic: Bridging the Gap Between Human Cognition and AI Reasoning

Authors:

Pages: 246-267
DOI: https://doi.org/10.54664/JGWG4148

Abstract:

Traditional AI systems rely on rigid logical structures that struggle with uncertainty, ambiguity, and the fluidity of human thought. African fuzzy logic, which mirrors indigenous African reasoning systems that accommodate degrees of truth rather than strict binary categories, offers a promising solution. This paper explores how African fuzzy logic can contribute to AI reasoning models by allowing machines to process information in a way that better reflects human cognitive flexibility. Drawing from African oral traditions, decision-making patterns, and indigenous dispute resolution mechanisms, the study presents a case for integrating multi-valued logic systems into AI. It demonstrates how African fuzzy logic can enhance AI applications in language processing, real-world problem-solving, and context-sensitive decision-making. Additionally, the paper examines how AI models can learn from the African logic of approximation, enabling them to reason in more nuanced and adaptive ways. The research highlights practical applications, including AI-driven legal reasoning, medical diagnostics in uncertain conditions, and adaptive AI for cross-cultural communication. Ultimately, the study argues that African fuzzy logic can serve as a crucial missing link in the development of AI systems that think more like humans.

Keywords:

African fuzzy logic, artificial intelligence, indigenous knowledge systems, context-sensitive.

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